DMP-KDO-PCLoSA: a LiDAR point cloud visibility analysis method for urban remote sensing using depth map projection and KNN optimization
Accurate line-of-sight (LoS) analysis in LiDAR point clouds is vital for urban remote sensing and geospatial applications, yet sparse and occluded data challenge reliability. We propose DMP-KDO-PCLoSA, a novel method integrating Depth Map Projection (DMP) with K-Nearest Neighbour Depth Optimization...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2540948 |
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| Summary: | Accurate line-of-sight (LoS) analysis in LiDAR point clouds is vital for urban remote sensing and geospatial applications, yet sparse and occluded data challenge reliability. We propose DMP-KDO-PCLoSA, a novel method integrating Depth Map Projection (DMP) with K-Nearest Neighbour Depth Optimization (KDO), featuring a Variable Correction Threshold (VCTS) for adaptive depth refinement. Compared to density-based and voxel-based methods, our approach achieves 92.23% visibility detection accuracy (VDA), reduces false detection rates by 1.09%, and decreases computation time by 40%. Experiments on urban datasets demonstrate its robustness in complex city environments. DMP-KDO-PCLoSA enhances LiDAR point cloud processing for urban modelling, supporting smart city planning, environmental monitoring, and disaster management. Its scalability and efficiency offer practical solutions for large-scale geospatial analysis, advancing remote sensing applications in diverse urban contexts. |
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| ISSN: | 1010-6049 1752-0762 |